Face GenerationΒΆ

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the DataΒΆ

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the DataΒΆ

MNISTΒΆ

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7ff296d51a58>

CelebAΒΆ

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7ff296cc4630>

Preprocess the DataΒΆ

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural NetworkΒΆ

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPUΒΆ

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1
Default GPU Device: /gpu:0

InputΒΆ

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_images = tf.placeholder(tf.float32,[None, image_width, image_height, image_channels],name='input_images')
    input_z = tf.placeholder(tf.float32,[None,z_dim],name = 'input_z')
    learning_rate = tf.placeholder(tf.float32,name = "learing_rate")
    return input_images, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

DiscriminatorΒΆ

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        C1 = tf.layers.conv2d(images,64,3,strides=2, padding='same')
        M1 = tf.maximum(alpha*C1,C1)
        
        C2 = tf.layers.conv2d(M1,128,3,strides=2,padding='same')
        B2 = tf.layers.batch_normalization(C2, training=True)
        M2 = tf.maximum(alpha*B2,B2)
        
        C3 = tf.layers.conv2d(M2,256,3,strides=2,padding='same')
        B3 = tf.layers.batch_normalization(C3, training=True)
        M3 = tf.maximum(alpha*B3,B3)
#         print(M3.shape)
        flat = tf.reshape(M3, (-1, 4*4*256))
        
        logits = tf.layers.dense(flat,1)
        out = tf.sigmoid(logits)
    return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

GeneratorΒΆ

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function 
#     print(z)
    with tf.variable_scope('generator',reuse=(not is_train)):
        alpha = 0.2
        x1 = tf.layers.dense(z,7*7*256)
        x1 = tf.reshape(x1, (-1,7,7,256))
        x1 = tf.layers.batch_normalization(x1,training=is_train)

        C1 = tf.layers.conv2d_transpose(x1,128,3,strides=2,padding='same')
        B1 = tf.layers.batch_normalization(C1,training=is_train)
        M1 = tf.maximum(alpha*B1,B1)

        C2 = tf.layers.conv2d_transpose(M1,64,3,strides=2,padding='same')
        B2 = tf.layers.batch_normalization(C2,training=is_train)
        M2 = tf.maximum(alpha*B2,B2)
        
        logits = tf.layers.conv2d_transpose(M2,out_channel_dim,3,strides=1,padding='same')
        output = tf.tanh(logits)
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

LossΒΆ

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    al = 0.1
    g_out = generator(input_z, out_channel_dim, is_train=True)
    d_out_real,d_logits_real = discriminator(input_real, reuse=False)
    dg_out,dg_logits = discriminator(g_out, reuse=True)
    
    dg_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=dg_logits,labels=tf.ones_like(dg_out)))
    
    dr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,labels=tf.ones_like(d_out_real)*(1-al)))
    d_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=dg_logits,labels=tf.zeros_like(dg_out)))
    d_loss = dr_loss+d_loss
    return d_loss, dg_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

OptimizationΒΆ

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_var = tf.trainable_variables()
    dgr = [var for var in t_var if var.name.startswith('discriminator')]
    gen = [var for var in t_var if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_opt_train = tf.train.AdamOptimizer(beta1=beta1,learning_rate=learning_rate).minimize(d_loss,var_list=dgr)
        g_opt_train = tf.train.AdamOptimizer(beta1=beta1,learning_rate=learning_rate).minimize(g_loss,var_list=gen)
    return d_opt_train, g_opt_train


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network TrainingΒΆ

Show OutputΒΆ

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

TrainΒΆ

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_images, input_z, learning_rate1 = model_inputs(data_shape[1],data_shape[2],data_shape[3],z_dim)
    
    d_loss, dg_loss = model_loss(input_images, input_z, data_shape[3])
    
    d_opt_train, dg_opt_train = model_opt(d_loss, dg_loss, learning_rate, beta1)
    

    print_times = 20
    show_times = 200

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        step = 0
        
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):

            
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                feed = {input_images: batch_images, input_z: batch_z, learning_rate1: learning_rate}
                
                sess.run(d_opt_train, feed_dict=feed)  
                sess.run(dg_opt_train, feed_dict=feed)
                
                if step%print_times==0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_images: batch_images*2})
                    train_loss_g = dg_loss.eval({input_z: batch_z})
                    print("step {}, (epoch {}/{})...".format(step, epoch_i+1, epoch_count),
                          "discriminator loss: {:.3f}...".format(train_loss_d),
                          "generator loss: {:.3f}".format(train_loss_g))
                
                    
                if step%show_times==0:
                    show_generator_output(sess, 64, input_z, data_shape[3], data_image_mode)
                step += 1

MNISTΒΆ

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 32 z_dim = 100 learning_rate = 0.0003 beta1 = 0.5

""" DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg'))) with tf.Graph().as_default(): train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches, mnist_dataset.shape, mnist_dataset.image_mode)

In [13]:
batch_size = 32
z_dim = 100
learning_rate = 0.0006
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))

with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
step 0, (epoch 1/2)... discriminator loss: 5.895... generator loss: 0.006
step 20, (epoch 1/2)... discriminator loss: 3.211... generator loss: 3.312
step 40, (epoch 1/2)... discriminator loss: 2.260... generator loss: 0.769
step 60, (epoch 1/2)... discriminator loss: 2.889... generator loss: 1.831
step 80, (epoch 1/2)... discriminator loss: 3.727... generator loss: 2.253
step 100, (epoch 1/2)... discriminator loss: 4.822... generator loss: 3.528
step 120, (epoch 1/2)... discriminator loss: 10.804... generator loss: 8.721
step 140, (epoch 1/2)... discriminator loss: 6.569... generator loss: 1.316
step 160, (epoch 1/2)... discriminator loss: 4.467... generator loss: 0.566
step 180, (epoch 1/2)... discriminator loss: 4.534... generator loss: 2.264
step 200, (epoch 1/2)... discriminator loss: 5.849... generator loss: 4.463
step 220, (epoch 1/2)... discriminator loss: 6.149... generator loss: 1.759
step 240, (epoch 1/2)... discriminator loss: 5.016... generator loss: 1.801
step 260, (epoch 1/2)... discriminator loss: 2.060... generator loss: 1.398
step 280, (epoch 1/2)... discriminator loss: 3.066... generator loss: 1.391
step 300, (epoch 1/2)... discriminator loss: 2.985... generator loss: 1.922
step 320, (epoch 1/2)... discriminator loss: 3.424... generator loss: 1.642
step 340, (epoch 1/2)... discriminator loss: 5.563... generator loss: 3.328
step 360, (epoch 1/2)... discriminator loss: 4.700... generator loss: 2.135
step 380, (epoch 1/2)... discriminator loss: 1.410... generator loss: 3.145
step 400, (epoch 1/2)... discriminator loss: 2.273... generator loss: 1.263
step 420, (epoch 1/2)... discriminator loss: 2.535... generator loss: 1.729
step 440, (epoch 1/2)... discriminator loss: 2.274... generator loss: 1.948
step 460, (epoch 1/2)... discriminator loss: 2.528... generator loss: 2.946
step 480, (epoch 1/2)... discriminator loss: 2.800... generator loss: 1.854
step 500, (epoch 1/2)... discriminator loss: 3.733... generator loss: 2.615
step 520, (epoch 1/2)... discriminator loss: 2.099... generator loss: 1.634
step 540, (epoch 1/2)... discriminator loss: 3.444... generator loss: 2.644
step 560, (epoch 1/2)... discriminator loss: 1.794... generator loss: 1.180
step 580, (epoch 1/2)... discriminator loss: 2.845... generator loss: 1.510
step 600, (epoch 1/2)... discriminator loss: 2.378... generator loss: 1.705
step 620, (epoch 1/2)... discriminator loss: 2.345... generator loss: 1.771
step 640, (epoch 1/2)... discriminator loss: 0.963... generator loss: 1.778
step 660, (epoch 1/2)... discriminator loss: 2.857... generator loss: 2.393
step 680, (epoch 1/2)... discriminator loss: 2.787... generator loss: 1.953
step 700, (epoch 1/2)... discriminator loss: 1.649... generator loss: 1.749
step 720, (epoch 1/2)... discriminator loss: 2.660... generator loss: 1.899
step 740, (epoch 1/2)... discriminator loss: 3.264... generator loss: 2.729
step 760, (epoch 1/2)... discriminator loss: 3.591... generator loss: 1.926
step 780, (epoch 1/2)... discriminator loss: 3.711... generator loss: 5.479
step 800, (epoch 1/2)... discriminator loss: 4.093... generator loss: 3.152
step 820, (epoch 1/2)... discriminator loss: 1.637... generator loss: 2.578
step 840, (epoch 1/2)... discriminator loss: 3.605... generator loss: 1.986
step 860, (epoch 1/2)... discriminator loss: 4.067... generator loss: 2.750
step 880, (epoch 1/2)... discriminator loss: 4.411... generator loss: 1.048
step 900, (epoch 1/2)... discriminator loss: 1.644... generator loss: 2.459
step 920, (epoch 1/2)... discriminator loss: 2.450... generator loss: 2.255
step 940, (epoch 1/2)... discriminator loss: 2.413... generator loss: 2.069
step 960, (epoch 1/2)... discriminator loss: 2.401... generator loss: 3.249
step 980, (epoch 1/2)... discriminator loss: 3.268... generator loss: 3.503
step 1000, (epoch 1/2)... discriminator loss: 3.314... generator loss: 0.314
step 1020, (epoch 1/2)... discriminator loss: 1.822... generator loss: 0.989
step 1040, (epoch 1/2)... discriminator loss: 1.261... generator loss: 3.001
step 1060, (epoch 1/2)... discriminator loss: 1.592... generator loss: 1.273
step 1080, (epoch 1/2)... discriminator loss: 1.131... generator loss: 3.638
step 1100, (epoch 1/2)... discriminator loss: 1.594... generator loss: 2.667
step 1120, (epoch 1/2)... discriminator loss: 1.346... generator loss: 2.617
step 1140, (epoch 1/2)... discriminator loss: 3.063... generator loss: 3.638
step 1160, (epoch 1/2)... discriminator loss: 2.557... generator loss: 1.894
step 1180, (epoch 1/2)... discriminator loss: 2.186... generator loss: 1.649
step 1200, (epoch 1/2)... discriminator loss: 2.308... generator loss: 2.164
step 1220, (epoch 1/2)... discriminator loss: 2.562... generator loss: 4.268
step 1240, (epoch 1/2)... discriminator loss: 2.129... generator loss: 2.250
step 1260, (epoch 1/2)... discriminator loss: 3.450... generator loss: 5.482
step 1280, (epoch 1/2)... discriminator loss: 3.203... generator loss: 5.531
step 1300, (epoch 1/2)... discriminator loss: 1.532... generator loss: 1.588
step 1320, (epoch 1/2)... discriminator loss: 1.812... generator loss: 0.996
step 1340, (epoch 1/2)... discriminator loss: 1.193... generator loss: 2.820
step 1360, (epoch 1/2)... discriminator loss: 2.149... generator loss: 2.082
step 1380, (epoch 1/2)... discriminator loss: 3.015... generator loss: 2.125
step 1400, (epoch 1/2)... discriminator loss: 1.762... generator loss: 1.994
step 1420, (epoch 1/2)... discriminator loss: 3.057... generator loss: 5.110
step 1440, (epoch 1/2)... discriminator loss: 3.868... generator loss: 1.910
step 1460, (epoch 1/2)... discriminator loss: 1.562... generator loss: 3.183
step 1480, (epoch 1/2)... discriminator loss: 2.051... generator loss: 3.220
step 1500, (epoch 1/2)... discriminator loss: 2.443... generator loss: 3.379
step 1520, (epoch 1/2)... discriminator loss: 1.797... generator loss: 1.809
step 1540, (epoch 1/2)... discriminator loss: 2.233... generator loss: 2.953
step 1560, (epoch 1/2)... discriminator loss: 2.143... generator loss: 1.970
step 1580, (epoch 1/2)... discriminator loss: 2.086... generator loss: 3.390
step 1600, (epoch 1/2)... discriminator loss: 2.988... generator loss: 4.115
step 1620, (epoch 1/2)... discriminator loss: 2.500... generator loss: 3.693
step 1640, (epoch 1/2)... discriminator loss: 2.162... generator loss: 3.823
step 1660, (epoch 1/2)... discriminator loss: 3.275... generator loss: 2.523
step 1680, (epoch 1/2)... discriminator loss: 2.532... generator loss: 1.776
step 1700, (epoch 1/2)... discriminator loss: 2.385... generator loss: 4.903
step 1720, (epoch 1/2)... discriminator loss: 2.878... generator loss: 3.452
step 1740, (epoch 1/2)... discriminator loss: 1.550... generator loss: 1.822
step 1760, (epoch 1/2)... discriminator loss: 1.088... generator loss: 1.935
step 1780, (epoch 1/2)... discriminator loss: 1.695... generator loss: 1.813
step 1800, (epoch 1/2)... discriminator loss: 1.887... generator loss: 3.606
step 1820, (epoch 1/2)... discriminator loss: 2.143... generator loss: 2.563
step 1840, (epoch 1/2)... discriminator loss: 2.306... generator loss: 2.879
step 1860, (epoch 1/2)... discriminator loss: 2.417... generator loss: 2.688
step 1880, (epoch 2/2)... discriminator loss: 1.279... generator loss: 2.712
step 1900, (epoch 2/2)... discriminator loss: 1.712... generator loss: 3.108
step 1920, (epoch 2/2)... discriminator loss: 1.149... generator loss: 0.966
step 1940, (epoch 2/2)... discriminator loss: 0.852... generator loss: 1.615
step 1960, (epoch 2/2)... discriminator loss: 0.902... generator loss: 1.640
step 1980, (epoch 2/2)... discriminator loss: 1.337... generator loss: 3.094
step 2000, (epoch 2/2)... discriminator loss: 1.629... generator loss: 2.320
step 2020, (epoch 2/2)... discriminator loss: 1.439... generator loss: 2.641
step 2040, (epoch 2/2)... discriminator loss: 1.882... generator loss: 3.587
step 2060, (epoch 2/2)... discriminator loss: 2.171... generator loss: 2.884
step 2080, (epoch 2/2)... discriminator loss: 1.037... generator loss: 1.874
step 2100, (epoch 2/2)... discriminator loss: 1.272... generator loss: 2.510
step 2120, (epoch 2/2)... discriminator loss: 1.729... generator loss: 2.550
step 2140, (epoch 2/2)... discriminator loss: 2.098... generator loss: 2.585
step 2160, (epoch 2/2)... discriminator loss: 1.838... generator loss: 5.644
step 2180, (epoch 2/2)... discriminator loss: 2.358... generator loss: 3.056
step 2200, (epoch 2/2)... discriminator loss: 3.323... generator loss: 2.471
step 2220, (epoch 2/2)... discriminator loss: 1.276... generator loss: 0.896
step 2240, (epoch 2/2)... discriminator loss: 1.676... generator loss: 2.799
step 2260, (epoch 2/2)... discriminator loss: 1.730... generator loss: 3.364
step 2280, (epoch 2/2)... discriminator loss: 1.629... generator loss: 3.710
step 2300, (epoch 2/2)... discriminator loss: 2.083... generator loss: 2.478
step 2320, (epoch 2/2)... discriminator loss: 2.068... generator loss: 3.175
step 2340, (epoch 2/2)... discriminator loss: 2.121... generator loss: 3.725
step 2360, (epoch 2/2)... discriminator loss: 3.270... generator loss: 4.151
step 2380, (epoch 2/2)... discriminator loss: 3.150... generator loss: 2.825
step 2400, (epoch 2/2)... discriminator loss: 2.097... generator loss: 2.689
step 2420, (epoch 2/2)... discriminator loss: 2.851... generator loss: 3.416
step 2440, (epoch 2/2)... discriminator loss: 2.926... generator loss: 3.799
step 2460, (epoch 2/2)... discriminator loss: 2.398... generator loss: 4.076
step 2480, (epoch 2/2)... discriminator loss: 3.535... generator loss: 4.083
step 2500, (epoch 2/2)... discriminator loss: 3.317... generator loss: 5.296
step 2520, (epoch 2/2)... discriminator loss: 1.266... generator loss: 1.979
step 2540, (epoch 2/2)... discriminator loss: 1.202... generator loss: 3.806
step 2560, (epoch 2/2)... discriminator loss: 1.551... generator loss: 3.598
step 2580, (epoch 2/2)... discriminator loss: 1.716... generator loss: 1.905
step 2600, (epoch 2/2)... discriminator loss: 1.685... generator loss: 4.040
step 2620, (epoch 2/2)... discriminator loss: 2.336... generator loss: 5.262
step 2640, (epoch 2/2)... discriminator loss: 2.755... generator loss: 4.424
step 2660, (epoch 2/2)... discriminator loss: 2.482... generator loss: 2.933
step 2680, (epoch 2/2)... discriminator loss: 2.664... generator loss: 1.971
step 2700, (epoch 2/2)... discriminator loss: 1.052... generator loss: 1.680
step 2720, (epoch 2/2)... discriminator loss: 1.459... generator loss: 3.477
step 2740, (epoch 2/2)... discriminator loss: 1.141... generator loss: 3.511
step 2760, (epoch 2/2)... discriminator loss: 1.458... generator loss: 2.592
step 2780, (epoch 2/2)... discriminator loss: 1.718... generator loss: 2.523
step 2800, (epoch 2/2)... discriminator loss: 1.169... generator loss: 0.946
step 2820, (epoch 2/2)... discriminator loss: 2.261... generator loss: 2.193
step 2840, (epoch 2/2)... discriminator loss: 2.864... generator loss: 3.851
step 2860, (epoch 2/2)... discriminator loss: 3.372... generator loss: 3.959
step 2880, (epoch 2/2)... discriminator loss: 2.416... generator loss: 3.122
step 2900, (epoch 2/2)... discriminator loss: 3.057... generator loss: 3.943
step 2920, (epoch 2/2)... discriminator loss: 3.307... generator loss: 4.645
step 2940, (epoch 2/2)... discriminator loss: 1.758... generator loss: 2.407
step 2960, (epoch 2/2)... discriminator loss: 3.492... generator loss: 4.649
step 2980, (epoch 2/2)... discriminator loss: 2.162... generator loss: 3.444
step 3000, (epoch 2/2)... discriminator loss: 2.840... generator loss: 4.064
step 3020, (epoch 2/2)... discriminator loss: 3.117... generator loss: 3.097
step 3040, (epoch 2/2)... discriminator loss: 0.989... generator loss: 2.187
step 3060, (epoch 2/2)... discriminator loss: 2.230... generator loss: 4.152
step 3080, (epoch 2/2)... discriminator loss: 3.086... generator loss: 2.428
step 3100, (epoch 2/2)... discriminator loss: 2.403... generator loss: 2.633
step 3120, (epoch 2/2)... discriminator loss: 0.986... generator loss: 1.842
step 3140, (epoch 2/2)... discriminator loss: 0.842... generator loss: 1.557
step 3160, (epoch 2/2)... discriminator loss: 0.921... generator loss: 2.151
step 3180, (epoch 2/2)... discriminator loss: 0.750... generator loss: 2.371
step 3200, (epoch 2/2)... discriminator loss: 0.697... generator loss: 2.952
step 3220, (epoch 2/2)... discriminator loss: 0.846... generator loss: 2.169
step 3240, (epoch 2/2)... discriminator loss: 0.881... generator loss: 2.787
step 3260, (epoch 2/2)... discriminator loss: 1.359... generator loss: 4.194
step 3280, (epoch 2/2)... discriminator loss: 0.836... generator loss: 2.215
step 3300, (epoch 2/2)... discriminator loss: 1.146... generator loss: 4.009
step 3320, (epoch 2/2)... discriminator loss: 0.909... generator loss: 1.574
step 3340, (epoch 2/2)... discriminator loss: 0.950... generator loss: 3.120
step 3360, (epoch 2/2)... discriminator loss: 1.529... generator loss: 3.028
step 3380, (epoch 2/2)... discriminator loss: 0.993... generator loss: 2.291
step 3400, (epoch 2/2)... discriminator loss: 0.832... generator loss: 3.145
step 3420, (epoch 2/2)... discriminator loss: 1.514... generator loss: 2.335
step 3440, (epoch 2/2)... discriminator loss: 0.891... generator loss: 2.706
step 3460, (epoch 2/2)... discriminator loss: 1.353... generator loss: 4.266
step 3480, (epoch 2/2)... discriminator loss: 1.728... generator loss: 2.954
step 3500, (epoch 2/2)... discriminator loss: 2.216... generator loss: 3.083
step 3520, (epoch 2/2)... discriminator loss: 1.803... generator loss: 2.903
step 3540, (epoch 2/2)... discriminator loss: 1.539... generator loss: 3.173
step 3560, (epoch 2/2)... discriminator loss: 2.015... generator loss: 4.978
step 3580, (epoch 2/2)... discriminator loss: 2.276... generator loss: 4.830
step 3600, (epoch 2/2)... discriminator loss: 2.185... generator loss: 3.270
step 3620, (epoch 2/2)... discriminator loss: 1.493... generator loss: 2.960
step 3640, (epoch 2/2)... discriminator loss: 1.236... generator loss: 3.052
step 3660, (epoch 2/2)... discriminator loss: 1.854... generator loss: 3.966
step 3680, (epoch 2/2)... discriminator loss: 2.096... generator loss: 4.307
step 3700, (epoch 2/2)... discriminator loss: 1.893... generator loss: 3.869
step 3720, (epoch 2/2)... discriminator loss: 2.682... generator loss: 2.952
step 3740, (epoch 2/2)... discriminator loss: 2.048... generator loss: 2.275

CelebAΒΆ

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [14]:
batch_size = 32
z_dim = 100
learning_rate = 0.0006
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
step 0, (epoch 1/1)... discriminator loss: 8.525... generator loss: 0.000
step 20, (epoch 1/1)... discriminator loss: 3.365... generator loss: 0.063
step 40, (epoch 1/1)... discriminator loss: 1.259... generator loss: 0.662
step 60, (epoch 1/1)... discriminator loss: 1.419... generator loss: 2.246
step 80, (epoch 1/1)... discriminator loss: 2.432... generator loss: 3.858
step 100, (epoch 1/1)... discriminator loss: 4.017... generator loss: 2.463
step 120, (epoch 1/1)... discriminator loss: 4.043... generator loss: 2.571
step 140, (epoch 1/1)... discriminator loss: 6.708... generator loss: 1.987
step 160, (epoch 1/1)... discriminator loss: 8.220... generator loss: 4.297
step 180, (epoch 1/1)... discriminator loss: 7.283... generator loss: 7.256
step 200, (epoch 1/1)... discriminator loss: 2.244... generator loss: 1.040
step 220, (epoch 1/1)... discriminator loss: 4.345... generator loss: 2.640
step 240, (epoch 1/1)... discriminator loss: 2.226... generator loss: 2.853
step 260, (epoch 1/1)... discriminator loss: 3.010... generator loss: 5.101
step 280, (epoch 1/1)... discriminator loss: 2.827... generator loss: 3.919
step 300, (epoch 1/1)... discriminator loss: 3.289... generator loss: 3.250
step 320, (epoch 1/1)... discriminator loss: 3.867... generator loss: 0.491
step 340, (epoch 1/1)... discriminator loss: 3.984... generator loss: 6.454
step 360, (epoch 1/1)... discriminator loss: 3.562... generator loss: 2.140
step 380, (epoch 1/1)... discriminator loss: 6.277... generator loss: 0.390
step 400, (epoch 1/1)... discriminator loss: 2.907... generator loss: 0.749
step 420, (epoch 1/1)... discriminator loss: 2.434... generator loss: 2.053
step 440, (epoch 1/1)... discriminator loss: 3.630... generator loss: 3.982
step 460, (epoch 1/1)... discriminator loss: 5.060... generator loss: 3.744
step 480, (epoch 1/1)... discriminator loss: 5.069... generator loss: 7.059
step 500, (epoch 1/1)... discriminator loss: 3.912... generator loss: 1.499
step 520, (epoch 1/1)... discriminator loss: 5.298... generator loss: 2.621
step 540, (epoch 1/1)... discriminator loss: 6.355... generator loss: 5.240
step 560, (epoch 1/1)... discriminator loss: 4.236... generator loss: 2.180
step 580, (epoch 1/1)... discriminator loss: 3.209... generator loss: 2.748
step 600, (epoch 1/1)... discriminator loss: 4.080... generator loss: 2.156
step 620, (epoch 1/1)... discriminator loss: 5.462... generator loss: 7.964
step 640, (epoch 1/1)... discriminator loss: 5.512... generator loss: 4.079
step 660, (epoch 1/1)... discriminator loss: 5.848... generator loss: 1.539
step 680, (epoch 1/1)... discriminator loss: 7.029... generator loss: 4.756
step 700, (epoch 1/1)... discriminator loss: 5.812... generator loss: 6.260
step 720, (epoch 1/1)... discriminator loss: 2.918... generator loss: 9.440
step 740, (epoch 1/1)... discriminator loss: 2.748... generator loss: 5.024
step 760, (epoch 1/1)... discriminator loss: 5.140... generator loss: 7.220
step 780, (epoch 1/1)... discriminator loss: 4.113... generator loss: 1.867
step 800, (epoch 1/1)... discriminator loss: 3.574... generator loss: 0.879
step 820, (epoch 1/1)... discriminator loss: 2.784... generator loss: 3.482
step 840, (epoch 1/1)... discriminator loss: 4.814... generator loss: 10.371
step 860, (epoch 1/1)... discriminator loss: 4.872... generator loss: 5.869
step 880, (epoch 1/1)... discriminator loss: 4.229... generator loss: 4.937
step 900, (epoch 1/1)... discriminator loss: 5.429... generator loss: 4.443
step 920, (epoch 1/1)... discriminator loss: 7.409... generator loss: 8.255
step 940, (epoch 1/1)... discriminator loss: 4.143... generator loss: 0.247
step 960, (epoch 1/1)... discriminator loss: 2.110... generator loss: 1.065
step 980, (epoch 1/1)... discriminator loss: 3.548... generator loss: 1.581
step 1000, (epoch 1/1)... discriminator loss: 3.748... generator loss: 5.336
step 1020, (epoch 1/1)... discriminator loss: 3.757... generator loss: 0.661
step 1040, (epoch 1/1)... discriminator loss: 3.627... generator loss: 5.940
step 1060, (epoch 1/1)... discriminator loss: 4.419... generator loss: 4.108
step 1080, (epoch 1/1)... discriminator loss: 5.435... generator loss: 10.736
step 1100, (epoch 1/1)... discriminator loss: 4.444... generator loss: 0.366
step 1120, (epoch 1/1)... discriminator loss: 4.606... generator loss: 8.731
step 1140, (epoch 1/1)... discriminator loss: 5.367... generator loss: 5.749
step 1160, (epoch 1/1)... discriminator loss: 6.374... generator loss: 7.764
step 1180, (epoch 1/1)... discriminator loss: 5.801... generator loss: 9.828
step 1200, (epoch 1/1)... discriminator loss: 6.173... generator loss: 7.698
step 1220, (epoch 1/1)... discriminator loss: 6.099... generator loss: 2.391
step 1240, (epoch 1/1)... discriminator loss: 5.836... generator loss: 5.570
step 1260, (epoch 1/1)... discriminator loss: 5.730... generator loss: 5.902
step 1280, (epoch 1/1)... discriminator loss: 5.456... generator loss: 5.194
step 1300, (epoch 1/1)... discriminator loss: 5.394... generator loss: 4.985
step 1320, (epoch 1/1)... discriminator loss: 4.975... generator loss: 7.975
step 1340, (epoch 1/1)... discriminator loss: 5.148... generator loss: 2.755
step 1360, (epoch 1/1)... discriminator loss: 4.702... generator loss: 4.544
step 1380, (epoch 1/1)... discriminator loss: 5.400... generator loss: 6.933
step 1400, (epoch 1/1)... discriminator loss: 5.633... generator loss: 9.838
step 1420, (epoch 1/1)... discriminator loss: 5.411... generator loss: 8.075
step 1440, (epoch 1/1)... discriminator loss: 5.066... generator loss: 3.385
step 1460, (epoch 1/1)... discriminator loss: 5.142... generator loss: 6.218
step 1480, (epoch 1/1)... discriminator loss: 5.738... generator loss: 5.026
step 1500, (epoch 1/1)... discriminator loss: 5.375... generator loss: 7.138
step 1520, (epoch 1/1)... discriminator loss: 5.854... generator loss: 3.539
step 1540, (epoch 1/1)... discriminator loss: 5.203... generator loss: 5.458
step 1560, (epoch 1/1)... discriminator loss: 5.858... generator loss: 6.677
step 1580, (epoch 1/1)... discriminator loss: 5.800... generator loss: 4.726
step 1600, (epoch 1/1)... discriminator loss: 7.139... generator loss: 3.791
step 1620, (epoch 1/1)... discriminator loss: 2.450... generator loss: 6.026
step 1640, (epoch 1/1)... discriminator loss: 4.820... generator loss: 4.528
step 1660, (epoch 1/1)... discriminator loss: 6.020... generator loss: 3.613
step 1680, (epoch 1/1)... discriminator loss: 6.212... generator loss: 2.985
step 1700, (epoch 1/1)... discriminator loss: 6.125... generator loss: 2.772
step 1720, (epoch 1/1)... discriminator loss: 7.869... generator loss: 6.382
step 1740, (epoch 1/1)... discriminator loss: 7.263... generator loss: 3.786
step 1760, (epoch 1/1)... discriminator loss: 7.365... generator loss: 9.889
step 1780, (epoch 1/1)... discriminator loss: 7.342... generator loss: 8.236
step 1800, (epoch 1/1)... discriminator loss: 6.191... generator loss: 5.628
step 1820, (epoch 1/1)... discriminator loss: 7.104... generator loss: 4.299
step 1840, (epoch 1/1)... discriminator loss: 6.826... generator loss: 9.143
step 1860, (epoch 1/1)... discriminator loss: 6.694... generator loss: 6.629
step 1880, (epoch 1/1)... discriminator loss: 8.452... generator loss: 3.633
step 1900, (epoch 1/1)... discriminator loss: 7.054... generator loss: 10.417
step 1920, (epoch 1/1)... discriminator loss: 7.726... generator loss: 11.621
step 1940, (epoch 1/1)... discriminator loss: 3.830... generator loss: 5.756
step 1960, (epoch 1/1)... discriminator loss: 6.813... generator loss: 5.137
step 1980, (epoch 1/1)... discriminator loss: 3.422... generator loss: 2.053
step 2000, (epoch 1/1)... discriminator loss: 4.601... generator loss: 5.138
step 2020, (epoch 1/1)... discriminator loss: 5.534... generator loss: 3.967
step 2040, (epoch 1/1)... discriminator loss: 6.145... generator loss: 5.008
step 2060, (epoch 1/1)... discriminator loss: 2.717... generator loss: 6.512
step 2080, (epoch 1/1)... discriminator loss: 3.280... generator loss: 5.328
step 2100, (epoch 1/1)... discriminator loss: 5.559... generator loss: 2.881
step 2120, (epoch 1/1)... discriminator loss: 2.804... generator loss: 0.734
step 2140, (epoch 1/1)... discriminator loss: 3.917... generator loss: 4.512
step 2160, (epoch 1/1)... discriminator loss: 3.640... generator loss: 2.128
step 2180, (epoch 1/1)... discriminator loss: 3.751... generator loss: 0.870
step 2200, (epoch 1/1)... discriminator loss: 3.118... generator loss: 3.611
step 2220, (epoch 1/1)... discriminator loss: 2.125... generator loss: 1.610
step 2240, (epoch 1/1)... discriminator loss: 2.160... generator loss: 7.223
step 2260, (epoch 1/1)... discriminator loss: 5.240... generator loss: 3.821
step 2280, (epoch 1/1)... discriminator loss: 5.082... generator loss: 1.440
step 2300, (epoch 1/1)... discriminator loss: 4.229... generator loss: 4.203
step 2320, (epoch 1/1)... discriminator loss: 3.499... generator loss: 4.275
step 2340, (epoch 1/1)... discriminator loss: 4.720... generator loss: 1.088
step 2360, (epoch 1/1)... discriminator loss: 4.063... generator loss: 2.821
step 2380, (epoch 1/1)... discriminator loss: 3.784... generator loss: 0.531
step 2400, (epoch 1/1)... discriminator loss: 4.628... generator loss: 0.546
step 2420, (epoch 1/1)... discriminator loss: 3.608... generator loss: 2.675
step 2440, (epoch 1/1)... discriminator loss: 3.849... generator loss: 1.455
step 2460, (epoch 1/1)... discriminator loss: 7.029... generator loss: 3.965
step 2480, (epoch 1/1)... discriminator loss: 4.709... generator loss: 5.821
step 2500, (epoch 1/1)... discriminator loss: 4.919... generator loss: 8.137
step 2520, (epoch 1/1)... discriminator loss: 7.867... generator loss: 4.425
step 2540, (epoch 1/1)... discriminator loss: 4.808... generator loss: 0.457
step 2560, (epoch 1/1)... discriminator loss: 2.572... generator loss: 8.787
step 2580, (epoch 1/1)... discriminator loss: 6.554... generator loss: 9.863
step 2600, (epoch 1/1)... discriminator loss: 3.174... generator loss: 9.469
step 2620, (epoch 1/1)... discriminator loss: 4.432... generator loss: 0.838
step 2640, (epoch 1/1)... discriminator loss: 4.418... generator loss: 8.750
step 2660, (epoch 1/1)... discriminator loss: 4.269... generator loss: 3.617
step 2680, (epoch 1/1)... discriminator loss: 5.335... generator loss: 1.125
step 2700, (epoch 1/1)... discriminator loss: 3.548... generator loss: 6.698
step 2720, (epoch 1/1)... discriminator loss: 4.241... generator loss: 1.404
step 2740, (epoch 1/1)... discriminator loss: 3.635... generator loss: 1.355
step 2760, (epoch 1/1)... discriminator loss: 5.248... generator loss: 0.067
step 2780, (epoch 1/1)... discriminator loss: 3.033... generator loss: 6.593
step 2800, (epoch 1/1)... discriminator loss: 3.643... generator loss: 4.998
step 2820, (epoch 1/1)... discriminator loss: 4.078... generator loss: 1.330
step 2840, (epoch 1/1)... discriminator loss: 4.005... generator loss: 6.263
step 2860, (epoch 1/1)... discriminator loss: 3.222... generator loss: 1.158
step 2880, (epoch 1/1)... discriminator loss: 3.246... generator loss: 0.403
step 2900, (epoch 1/1)... discriminator loss: 3.719... generator loss: 9.101
step 2920, (epoch 1/1)... discriminator loss: 5.914... generator loss: 6.715
step 2940, (epoch 1/1)... discriminator loss: 3.806... generator loss: 3.892
step 2960, (epoch 1/1)... discriminator loss: 3.883... generator loss: 5.119
step 2980, (epoch 1/1)... discriminator loss: 4.696... generator loss: 7.085
step 3000, (epoch 1/1)... discriminator loss: 3.733... generator loss: 7.946
step 3020, (epoch 1/1)... discriminator loss: 3.720... generator loss: 4.183
step 3040, (epoch 1/1)... discriminator loss: 4.485... generator loss: 9.758
step 3060, (epoch 1/1)... discriminator loss: 6.088... generator loss: 0.977
step 3080, (epoch 1/1)... discriminator loss: 5.220... generator loss: 10.997
step 3100, (epoch 1/1)... discriminator loss: 3.035... generator loss: 2.008
step 3120, (epoch 1/1)... discriminator loss: 5.172... generator loss: 1.961
step 3140, (epoch 1/1)... discriminator loss: 5.231... generator loss: 4.786
step 3160, (epoch 1/1)... discriminator loss: 4.548... generator loss: 5.684
step 3180, (epoch 1/1)... discriminator loss: 4.393... generator loss: 7.249
step 3200, (epoch 1/1)... discriminator loss: 3.855... generator loss: 9.680
step 3220, (epoch 1/1)... discriminator loss: 4.042... generator loss: 9.837
step 3240, (epoch 1/1)... discriminator loss: 5.467... generator loss: 4.758
step 3260, (epoch 1/1)... discriminator loss: 5.043... generator loss: 7.963
step 3280, (epoch 1/1)... discriminator loss: 5.786... generator loss: 7.690
step 3300, (epoch 1/1)... discriminator loss: 4.004... generator loss: 1.431
step 3320, (epoch 1/1)... discriminator loss: 2.380... generator loss: 1.682
step 3340, (epoch 1/1)... discriminator loss: 7.173... generator loss: 5.691
step 3360, (epoch 1/1)... discriminator loss: 4.753... generator loss: 2.948
step 3380, (epoch 1/1)... discriminator loss: 4.331... generator loss: 7.203
step 3400, (epoch 1/1)... discriminator loss: 5.107... generator loss: 8.787
step 3420, (epoch 1/1)... discriminator loss: 3.194... generator loss: 7.959
step 3440, (epoch 1/1)... discriminator loss: 4.516... generator loss: 8.817
step 3460, (epoch 1/1)... discriminator loss: 4.962... generator loss: 2.965
step 3480, (epoch 1/1)... discriminator loss: 3.214... generator loss: 0.957
step 3500, (epoch 1/1)... discriminator loss: 3.205... generator loss: 2.349
step 3520, (epoch 1/1)... discriminator loss: 5.154... generator loss: 0.243
step 3540, (epoch 1/1)... discriminator loss: 5.738... generator loss: 2.353
step 3560, (epoch 1/1)... discriminator loss: 4.456... generator loss: 4.443
step 3580, (epoch 1/1)... discriminator loss: 3.174... generator loss: 1.400
step 3600, (epoch 1/1)... discriminator loss: 4.011... generator loss: 2.376
step 3620, (epoch 1/1)... discriminator loss: 3.199... generator loss: 1.235
step 3640, (epoch 1/1)... discriminator loss: 5.280... generator loss: 3.126
step 3660, (epoch 1/1)... discriminator loss: 2.750... generator loss: 2.152
step 3680, (epoch 1/1)... discriminator loss: 3.521... generator loss: 2.961
step 3700, (epoch 1/1)... discriminator loss: 2.747... generator loss: 6.371
step 3720, (epoch 1/1)... discriminator loss: 4.965... generator loss: 1.718
step 3740, (epoch 1/1)... discriminator loss: 3.109... generator loss: 4.336
step 3760, (epoch 1/1)... discriminator loss: 4.327... generator loss: 1.894
step 3780, (epoch 1/1)... discriminator loss: 3.650... generator loss: 1.439
step 3800, (epoch 1/1)... discriminator loss: 3.568... generator loss: 0.482
step 3820, (epoch 1/1)... discriminator loss: 6.415... generator loss: 3.766
step 3840, (epoch 1/1)... discriminator loss: 5.423... generator loss: 2.284
step 3860, (epoch 1/1)... discriminator loss: 3.273... generator loss: 1.419
step 3880, (epoch 1/1)... discriminator loss: 4.347... generator loss: 5.386
step 3900, (epoch 1/1)... discriminator loss: 4.022... generator loss: 5.297
step 3920, (epoch 1/1)... discriminator loss: 3.446... generator loss: 2.450
step 3940, (epoch 1/1)... discriminator loss: 4.817... generator loss: 1.219
step 3960, (epoch 1/1)... discriminator loss: 3.896... generator loss: 8.469
step 3980, (epoch 1/1)... discriminator loss: 9.259... generator loss: 6.626
step 4000, (epoch 1/1)... discriminator loss: 3.413... generator loss: 1.193
step 4020, (epoch 1/1)... discriminator loss: 5.485... generator loss: 4.957
step 4040, (epoch 1/1)... discriminator loss: 3.424... generator loss: 1.274
step 4060, (epoch 1/1)... discriminator loss: 3.510... generator loss: 0.881
step 4080, (epoch 1/1)... discriminator loss: 3.260... generator loss: 4.028
step 4100, (epoch 1/1)... discriminator loss: 3.815... generator loss: 1.191
step 4120, (epoch 1/1)... discriminator loss: 2.528... generator loss: 4.038
step 4140, (epoch 1/1)... discriminator loss: 2.471... generator loss: 4.629
step 4160, (epoch 1/1)... discriminator loss: 3.709... generator loss: 1.227
step 4180, (epoch 1/1)... discriminator loss: 2.726... generator loss: 3.768
step 4200, (epoch 1/1)... discriminator loss: 5.500... generator loss: 6.860
step 4220, (epoch 1/1)... discriminator loss: 5.715... generator loss: 4.906
step 4240, (epoch 1/1)... discriminator loss: 3.662... generator loss: 3.828
step 4260, (epoch 1/1)... discriminator loss: 3.584... generator loss: 2.133
step 4280, (epoch 1/1)... discriminator loss: 2.507... generator loss: 1.343
step 4300, (epoch 1/1)... discriminator loss: 3.180... generator loss: 4.527
step 4320, (epoch 1/1)... discriminator loss: 3.341... generator loss: 3.140
step 4340, (epoch 1/1)... discriminator loss: 4.308... generator loss: 2.463
step 4360, (epoch 1/1)... discriminator loss: 3.660... generator loss: 1.016
step 4380, (epoch 1/1)... discriminator loss: 2.798... generator loss: 3.885
step 4400, (epoch 1/1)... discriminator loss: 4.015... generator loss: 2.549
step 4420, (epoch 1/1)... discriminator loss: 3.328... generator loss: 4.660
step 4440, (epoch 1/1)... discriminator loss: 3.831... generator loss: 1.320
step 4460, (epoch 1/1)... discriminator loss: 3.421... generator loss: 1.408
step 4480, (epoch 1/1)... discriminator loss: 3.266... generator loss: 0.746
step 4500, (epoch 1/1)... discriminator loss: 4.164... generator loss: 4.677
step 4520, (epoch 1/1)... discriminator loss: 4.736... generator loss: 4.271
step 4540, (epoch 1/1)... discriminator loss: 3.349... generator loss: 1.374
step 4560, (epoch 1/1)... discriminator loss: 4.740... generator loss: 2.145
step 4580, (epoch 1/1)... discriminator loss: 4.206... generator loss: 6.288
step 4600, (epoch 1/1)... discriminator loss: 4.788... generator loss: 2.761
step 4620, (epoch 1/1)... discriminator loss: 3.124... generator loss: 2.263
step 4640, (epoch 1/1)... discriminator loss: 4.816... generator loss: 3.566
step 4660, (epoch 1/1)... discriminator loss: 3.084... generator loss: 4.880
step 4680, (epoch 1/1)... discriminator loss: 3.647... generator loss: 5.128
step 4700, (epoch 1/1)... discriminator loss: 3.698... generator loss: 1.024
step 4720, (epoch 1/1)... discriminator loss: 3.882... generator loss: 1.812
step 4740, (epoch 1/1)... discriminator loss: 3.203... generator loss: 8.118
step 4760, (epoch 1/1)... discriminator loss: 3.186... generator loss: 5.373
step 4780, (epoch 1/1)... discriminator loss: 3.722... generator loss: 2.227
step 4800, (epoch 1/1)... discriminator loss: 3.605... generator loss: 5.515
step 4820, (epoch 1/1)... discriminator loss: 3.080... generator loss: 2.862
step 4840, (epoch 1/1)... discriminator loss: 3.556... generator loss: 2.225
step 4860, (epoch 1/1)... discriminator loss: 5.001... generator loss: 3.955
step 4880, (epoch 1/1)... discriminator loss: 3.313... generator loss: 0.561
step 4900, (epoch 1/1)... discriminator loss: 5.880... generator loss: 2.233
step 4920, (epoch 1/1)... discriminator loss: 3.544... generator loss: 0.481
step 4940, (epoch 1/1)... discriminator loss: 4.170... generator loss: 0.417
step 4960, (epoch 1/1)... discriminator loss: 3.635... generator loss: 2.479
step 4980, (epoch 1/1)... discriminator loss: 3.807... generator loss: 1.340
step 5000, (epoch 1/1)... discriminator loss: 4.215... generator loss: 0.676
step 5020, (epoch 1/1)... discriminator loss: 5.400... generator loss: 3.395
step 5040, (epoch 1/1)... discriminator loss: 3.196... generator loss: 3.187
step 5060, (epoch 1/1)... discriminator loss: 2.757... generator loss: 0.760
step 5080, (epoch 1/1)... discriminator loss: 3.960... generator loss: 1.566
step 5100, (epoch 1/1)... discriminator loss: 2.986... generator loss: 0.988
step 5120, (epoch 1/1)... discriminator loss: 2.664... generator loss: 2.504
step 5140, (epoch 1/1)... discriminator loss: 4.415... generator loss: 0.309
step 5160, (epoch 1/1)... discriminator loss: 3.105... generator loss: 3.434
step 5180, (epoch 1/1)... discriminator loss: 3.575... generator loss: 2.016
step 5200, (epoch 1/1)... discriminator loss: 2.999... generator loss: 6.738
step 5220, (epoch 1/1)... discriminator loss: 3.652... generator loss: 0.893
step 5240, (epoch 1/1)... discriminator loss: 3.627... generator loss: 1.133
step 5260, (epoch 1/1)... discriminator loss: 3.030... generator loss: 1.938
step 5280, (epoch 1/1)... discriminator loss: 2.223... generator loss: 2.170
step 5300, (epoch 1/1)... discriminator loss: 3.317... generator loss: 0.821
step 5320, (epoch 1/1)... discriminator loss: 4.225... generator loss: 2.327
step 5340, (epoch 1/1)... discriminator loss: 3.033... generator loss: 5.330
step 5360, (epoch 1/1)... discriminator loss: 3.821... generator loss: 0.645
step 5380, (epoch 1/1)... discriminator loss: 3.097... generator loss: 1.884
step 5400, (epoch 1/1)... discriminator loss: 4.479... generator loss: 2.398
step 5420, (epoch 1/1)... discriminator loss: 3.529... generator loss: 0.807
step 5440, (epoch 1/1)... discriminator loss: 2.934... generator loss: 0.220
step 5460, (epoch 1/1)... discriminator loss: 3.196... generator loss: 0.758
step 5480, (epoch 1/1)... discriminator loss: 5.551... generator loss: 1.587
step 5500, (epoch 1/1)... discriminator loss: 2.443... generator loss: 2.589
step 5520, (epoch 1/1)... discriminator loss: 2.447... generator loss: 3.101
step 5540, (epoch 1/1)... discriminator loss: 3.594... generator loss: 1.973
step 5560, (epoch 1/1)... discriminator loss: 3.875... generator loss: 4.655
step 5580, (epoch 1/1)... discriminator loss: 4.554... generator loss: 0.525
step 5600, (epoch 1/1)... discriminator loss: 4.335... generator loss: 2.439
step 5620, (epoch 1/1)... discriminator loss: 2.777... generator loss: 7.457
step 5640, (epoch 1/1)... discriminator loss: 3.485... generator loss: 2.073
step 5660, (epoch 1/1)... discriminator loss: 4.065... generator loss: 2.518
step 5680, (epoch 1/1)... discriminator loss: 4.865... generator loss: 2.600
step 5700, (epoch 1/1)... discriminator loss: 3.453... generator loss: 5.430
step 5720, (epoch 1/1)... discriminator loss: 3.524... generator loss: 0.530
step 5740, (epoch 1/1)... discriminator loss: 3.135... generator loss: 1.788
step 5760, (epoch 1/1)... discriminator loss: 2.890... generator loss: 0.937
step 5780, (epoch 1/1)... discriminator loss: 2.688... generator loss: 0.835
step 5800, (epoch 1/1)... discriminator loss: 1.959... generator loss: 1.819
step 5820, (epoch 1/1)... discriminator loss: 2.720... generator loss: 0.997
step 5840, (epoch 1/1)... discriminator loss: 3.607... generator loss: 1.069
step 5860, (epoch 1/1)... discriminator loss: 5.155... generator loss: 2.673
step 5880, (epoch 1/1)... discriminator loss: 4.542... generator loss: 0.638
step 5900, (epoch 1/1)... discriminator loss: 3.075... generator loss: 2.334
step 5920, (epoch 1/1)... discriminator loss: 4.771... generator loss: 1.319
step 5940, (epoch 1/1)... discriminator loss: 3.344... generator loss: 1.117
step 5960, (epoch 1/1)... discriminator loss: 2.174... generator loss: 2.746
step 5980, (epoch 1/1)... discriminator loss: 3.574... generator loss: 0.364
step 6000, (epoch 1/1)... discriminator loss: 1.915... generator loss: 1.390
step 6020, (epoch 1/1)... discriminator loss: 3.235... generator loss: 3.915
step 6040, (epoch 1/1)... discriminator loss: 3.734... generator loss: 2.002
step 6060, (epoch 1/1)... discriminator loss: 3.017... generator loss: 1.984
step 6080, (epoch 1/1)... discriminator loss: 3.214... generator loss: 2.761
step 6100, (epoch 1/1)... discriminator loss: 3.686... generator loss: 0.728
step 6120, (epoch 1/1)... discriminator loss: 3.545... generator loss: 1.760
step 6140, (epoch 1/1)... discriminator loss: 3.008... generator loss: 0.808
step 6160, (epoch 1/1)... discriminator loss: 3.000... generator loss: 0.570
step 6180, (epoch 1/1)... discriminator loss: 3.719... generator loss: 3.867
step 6200, (epoch 1/1)... discriminator loss: 3.499... generator loss: 1.897
step 6220, (epoch 1/1)... discriminator loss: 3.155... generator loss: 1.582
step 6240, (epoch 1/1)... discriminator loss: 5.098... generator loss: 2.387
step 6260, (epoch 1/1)... discriminator loss: 2.910... generator loss: 1.023
step 6280, (epoch 1/1)... discriminator loss: 2.749... generator loss: 0.553
step 6300, (epoch 1/1)... discriminator loss: 3.757... generator loss: 0.794
step 6320, (epoch 1/1)... discriminator loss: 3.933... generator loss: 0.291

Submitting This ProjectΒΆ

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.